P1 | Basic tools for data visualization

.title[
# P1 | Basic tools for data visualization
]
.subtitle[
## Introduction
]
.author[
### Marta Coronado Zamora and Miriam Merenciano
]
.date[
### 19 February 2026
]

---

# Keep in touch

| Miriam Merenciano |
|:-:|
| <a href="mailto:miriam.merenciano@uab.cat"> miriam.merenciano@uab.cat </a> | 
| <a href="https://portalrecerca.uab.cat/es/organisations/grup-de-gen%C3%B2mica-bioinform%C3%A0tica-i-biologia-evolutiva-gbbe">&nbsp; Universitat Autònoma de Barcelona </a> |

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# Practical session dynamics

**Content** (P1-P5)
- Introduction 
- Exercises - complete and submit to [ATENEA](https://atenea.upc.edu/login/index.php)
- Project (divided in to 2 assignments)

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# Evaluation

- **10% participation**

Individual submission at the end of each theory/practical session

- **40% group assignments** (minimum grade 4/10)

4 assignments, each 10%

- **20% mid-term exam**

- **30% final exam**

The weighted grade of the midterm exam and the final exam requires a minimum score of 3.5 out of 10 to consider the other parts of the evaluation. 
 
 
 
 [Information in the Syllabus](https://www.fib.upc.edu/en/studies/bachelors-degrees/bachelor-degree-bioinformatics/curriculum/syllabus/DV-BBI)

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# Basic `R` knowledge

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### Tidy data

Data frames with one observation per row and one variable per column.

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### Tidy data

Two types of ordered data structures:

- **Wide format** (the most common): in a wide format, multiple measurements of a single observation are stored in a single row.

```
##   Student Math Literature PE
## 1       A   99         45 56
## 2       B   73         78 55
## 3       C   12         96 57
```

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- **Long format**: each row corresponds to one measurement of an observation.

```
## # A tibble: 9 × 3
## Student Subject Score
## <chr> <chr> <dbl>
## 1 A Math 99
## 2 A Literature 45
## 3 A PE 56
## 4 B Math 73
## 5 B Literature 78
## 6 B PE 55
## 7 C Math 12
## 8 C Literature 96
## 9 C PE 57
```

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### Tidy data

There are functions to convert from wide format to long format:

``` r
library(tidyr)

long_df <- pivot_longer(
 wide_df,
 cols = c(Math, Literature, PE), # o cols = -c(Student),
 names_to = "Subject",
 values_to = "Score"
 )
long_df
```

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# Getting help

- `?read.table`, `?str`, `?as.factor`
- Press F1 (in RStudio)
- [Stack Overflow](https://stackoverflow.com) ([`R`](https://stackoverflow.com/questions/tagged/r), [`ggplot2`](https://stackoverflow.com/questions/tagged/ggplot2))
- ChatGPT
- Ask your classmates or your teacher

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# First task (just for fun)

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# First task

1. Make sure **RStudio** is working

2. Install some packages you will need for this practical sessions:

+ `ggplot2`

+ `tidyr`
  
  + `shiny`
  
  + `plotly`

3. Work in the following exercises

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## Exercise: create some testing plots

Execute the following chunks using the [`iris`](https://en.wikipedia.org/wiki/Iris_flower_data_set) dataset and think what is going on:

``` r
library(ggplot2)
head(iris)
str(iris)
```

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## Exercise: create some testing plots

What figure does the following command generate?

``` r
ggplot(data = iris, mapping = aes(x = Species, y = Petal.Length, fill = Species)) +
  geom_boxplot()
```

Here we can see the distribution of the variable petal length regarding the species it belongs

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## Exercise: create some testing plots

What figure does the following command generate?

``` r
ggplot(data=iris,aes(x=Sepal.Width, y=Sepal.Length, color=Species)) + geom_point() + theme_minimal()
```

--

Here we can see using a **scatter plot**, the variable sepal width and sepal length and coloured regarding the species it belongs

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## Exercise: describe a data set

Read the file in this [link](https://raw.githubusercontent.com/marta-coronado/data_visualization/refs/heads/main/P/0/data/sample.txt), ensure it has a tidy format; indicate the data type of each variable; convert to long format.

Which column(s) will you use in the `cols` argument of the `pivot_longer` function?

You can read directly a file from a link `read.table("https://...txt")`

Solutions in the next slides 😇

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## Exercise: describe a data set

``` r
data <- read.table(file="https://raw.githubusercontent.com/marta-coronado/data_visualization/refs/heads/main/P/0/data/sample.txt", header=T)
head(data)
```

```
##      ID Group     Width   Height    Depth
## 1 ID001     B  6.518109 41.16221 4.637743
## 2 ID002     B  6.505138 44.10542 4.822542
## 3 ID003     A 11.428349 45.03662 5.149416
## 4 ID004     A 13.714852 58.60545 5.834809
## 5 ID005     B  8.802453 47.40527 4.546300
## 6 ID006     B 10.038084 58.58873 5.981818
```

``` r
dim(data)
```

```
## [1] 100   5
```

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## Exercise: describe a data set

Indicate the data type of each variable.

``` r
sapply(data, class)
```

```
##          ID       Group       Width      Height       Depth 
## "character" "character"   "numeric"   "numeric"   "numeric"
```

``` r
str(data)
```

```
## 'data.frame':	100 obs. of  5 variables:
##  $ ID    : chr  "ID001" "ID002" "ID003" "ID004" ...
##  $ Group : chr  "B" "B" "A" "A" ...
##  $ Width : num  6.52 6.51 11.43 13.71 8.8 ...
##  $ Height: num  41.2 44.1 45 58.6 47.4 ...
##  $ Depth : num  4.64 4.82 5.15 5.83 4.55 ...
```

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## Exercise: describe a data set

Convert from wide to long format using `tidyr::pivot_longer()`

``` r
library(tidyr)
long_df <- pivot_longer(
 data,
 cols = -c("ID", "Group"),
 names_to = "metric",
 values_to = "value"
 )
## check dimensions
dim(long_df)
```

```
## [1] 300   4
```

``` r
dim(data)
```

```
## [1] 100   5
```

---

## Exercise: describe a data set

Convert from wide to long format using `tidyr::pivot_longer()`

``` r
library(tidyr)
long_df <- pivot_longer(
 data,
 cols = -c("ID", "Group"),
 names_to = "metric",
 values_to = "value"
 )

head(long_df)
```

```
## # A tibble: 6 × 4
## ID Group metric value
## <chr> <chr> <chr> <dbl>
## 1 ID001 B Width 6.52
## 2 ID001 B Height 41.2 
## 3 ID001 B Depth 4.64
## 4 ID002 B Width 6.51
## 5 ID002 B Height 44.1 
## 6 ID002 B Depth 4.82
```

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# Get started!
## **Tools for data visualization**

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# Practice 
## Introduction to `ggplot2`

- Open the document `P1_exercises.Rmd` in RStudio and complete the exercises.
- Upload the completed document to [ATENEA](https://atenea.upc.edu/login/index.php) at the end of the session. Important: both **.Rmd** and **.html**.

- Evaluation:
  - **5/10** only for uploading a complete document. 
  - **-1 point** if you use local paths without uploading the working data.
  - **-1 point** if you do not upload the .html file.
  - **-1 point** if you do not write your name in the uploaded files.

---

# Project

## Group project

The project has 3 different parts (A, B and C) divided in two big assignments.
- You can deliver the parts separately to get feedback before submitting the final version
- Each part must be submitted before next practical session
- The first assignment will contain parts A and B
- The second assignment will contain part C
- ~15 minutes in the end of each class devoted to discuss your problems

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# Project

## Group project

__Part A__

- __1\.__ Create groups of \~4 people
- __2\.__ Choose a data set with the following requirements
    + Tabular format (txt, csv, tsv...)
    + More than 80 observations
    + At least 6 variables
    + At least 2 discrete and 3 continuous variables
    + Data with biological meaning
    + Different from the ones chosen by other groups

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# Project

## Group project

- __3\.__ Describe your data set:
    + Where and why was the information collected?
    + Which is the meaning of each variable?
    + Do the variables have unit? Which one?
    + Does the data set have a long format?
- __4\.__ Write the code to:
    + Read it into R
    + Reshape the data if necessary into long format
    + Check the variable classes and update them if necessary

Write 3 and 4 in an `R Markdown` document and __submit it before next practical session__ (one per group).

If you need help formatting the R Markdown, ask me for a guide of an introduction to R Markdown.

---

## Data sets from research articles

- ["Zika virus evolution and spread in the Americas"](https://www.nature.com/articles/nature22402#supplementary-information) (Table S2)
- ["Great ape genetic diversity and population history"](https://www.nature.com/articles/nature12228#supplementary-information) (Table S1 or S3)
- ["Transcriptome and genome sequencing uncovers functional variation in humans"](https://www.nature.com/articles/nature12531). [Table with cis eQTLs in EUR](https://www.ebi.ac.uk/arrayexpress/files/E-GEUV-1/EUR373.gene.cis.FDR5.best.rs137.txt.gz) ([description](https://www.ebi.ac.uk/arrayexpress/files/E-GEUV-1/GeuvadisRNASeqAnalysisFiles_README.txt))
- ["Signatures of archaic adaptive introgression in present-day human Populations"](https://academic.oup.com/mbe/article/34/2/296/2633371#supplementary-data) (Table S3)
- ["The evolutionary history of dogs in the Americas"](http://science.sciencemag.org/content/361/6397/81) (Table S1)
- ["Ancient genomes document multiple waves of migration in Southeast Asian prehistory"](http://science.sciencemag.org/content/361/6397/92) (Table S1)
- ["Population-scale long-read sequencing uncovers transposable elements associated with gene expression variation and adaptive signatures in _Drosophila_"](https://www.nature.com/articles/s41467-022-29518-8#Sec29) (Table S10)
- ["Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidates for targeted treatment"](https://www.nature.com/articles/s41467-021-25177-3#Sec31) (Table S1)
- ["Pan-cancer analysis of whole genomes"](https://www.nature.com/articles/s41586-020-1969-6#Sec23) (Table S1)
- ["The genomic basis of copper tolerance in _Drosophila_ is shaped by a complex interplay of regulatory and environmental factors"](https://static-content.springer.com/esm/art%3A10.1186%2Fs12915-022-01479-w/MediaObjects/12915_2022_1479_MOESM4_ESM.xlsx) (Table S3)
- ["Transposons contribute to the diversification of the head, gut, and ovary transcriptomes across _Drosophila_ natural strains"](https://raw.githubusercontent.com/GonzalezLab/chimerics-transcripts-dmelanogaster/main/01.%20Chimeric%20gene-TE%20transcripts/chimerics_v2.tab) (Chimeric gene-TE transcripts data)